System and method for providing interference parameter estimation for multi-input multi-output (MIMO) communication system

ABSTRACT

A method and apparatus are provided. The method includes receiving a desired signal from a serving base station, receiving a plurality of interfering signals from one or more base stations, estimating a maximum likelihood (ML) decision metric of interfering signals, applying a logarithm function to the ML decision metric, and applying a maximum-log approximation function to a serving data vector and an interference data vector, which are included in the ML decision metric, determining the values of a transmit power, a rank, a precoding matrix, a modulation order and a transmission scheme using the applied ML decision metric, and cancelling the interfering signals from the received signals using the determined values of transmit power, rank, precoding matrix, modulation order and transmission scheme.

PRIORITY

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/370,464, which was filed in theU.S. Patent and Trademark Office on Aug. 3, 2016, and to U.S.Provisional Patent Application No. 62/384,504, which was filed in theU.S. Patent and Trademark Office on Sep. 7, 2016, the entire content ofeach of which is incorporated herein by reference.

FIELD

The present disclosure generally relates to a method and apparatus, andmore particularly, to a method and apparatus for interference parameterestimation in multi-input multi-output (MIMO) communication systems.

BACKGROUND

Users of electronic devices require increasing functionality in theapplications and services provided by electronic devices and thecommunication networks used by electronic devices. Wirelesscommunication networks using MIMO provide increased capacity for dataand voice communications for the users of electronic devices. One of thechallenges faced by wireless communication networks using MIMO ismitigation of undesired signals causing interference to desired signalsreceived in a mobile terminal, particularly at a cell-edge whereinterfering signals from other cells may be stronger. Methods formitigation of signal interference are necessary to improve cell-edgeperformance.

One of the requirements for mitigating the effects of interferingsignals is knowledge of interference parameters of the interferingsignals. However, interference parameters may not be provided to theelectronic device via signaling or other methods, requiring theelectronic device to estimate, or blind detect, the interferenceparameters. Interference parameter estimation is a required procedurefor third generation partnership project (3GPP) long term evolution(LTE) Release-12 network assisted interference cancellation andsuppression (NAICS). Interference parameters may be estimated by using amaximum likelihood (ML) method. However, the complexity of the ML methodis relatively large especially for a MIMO communication network.

SUMMARY

An aspect of the present disclosure provides an interferencecancellation method on the basis of NAICS interference parameters thatare determined by blind-detection methods.

Another aspect of the present disclosure provides a blind detectionmethod with low computational complexity in order to estimateinterference parameters from adjacent interfering cells includingtransmit power level, rank, precoding matrix, modulation order (MOD) andtransmission scheme.

Another aspect of the present disclosure provides a method forcompensating for degradation of communication performance resulting fromblind detection of interference parameters using low complexity methods.

Another aspect of the present disclosure provides a method whichincludes, but is not limited to, receiving a desired signal from aserving base station, receiving a plurality of interfering signals fromone or more base stations, estimating a maximum likelihood (ML) decisionmetric of the plurality of interfering signals, applying a logarithmfunction to the ML decision metric, and applying a maximum-logapproximation function to a serving data vector and an interference datavector, which are included in the ML decision metric, determining thevalues of a transmit power, a rank, a precoding matrix, a modulation,and a transmission scheme using the applied ML decision metric, andcancelling the interfering signals from the received signal using thedetermined values of transmit power, rank, precoding matrix, modulationorder and transmission scheme.

Another aspect of the present disclosure provides an apparatus whichincludes, but is not limited a processor configured to receive a desiredsignal from a serving base station, receive a plurality of interferingsignals from one or more base stations, estimate a maximum likelihood(ML) decision metric of the plurality of interfering signals, apply alogarithm function to the ML decision metric, and apply a maximum-logapproximation function to a serving data vector and an interference datavector, which are included in the ML decision metric, determine thevalues of a transmit power, a rank, a precoding matrix, a modulationorder and a transmission scheme using the applied ML decision metric,and cancel the interfering signals from the received signal using thedetermined values of transmit power, rank, precoding matrix, modulationorder and transmission scheme.

Another aspect of the present disclosure provides a method ofmanufacturing a processor which includes, but is not limited to, formingthe processor as part of a wafer or package that includes at least oneother process, wherein the processor is configured to receive a desiredsignal from a serving base station, receive a plurality of interferingsignals from one or more base stations, estimate a maximum likelihood(ML) decision metric of the plurality of interfering signals, apply alogarithm function to the ML decision metric, and apply a maximum-logapproximation function to a serving data vector and an interference datavector, which are included in the ML decision metric, determine thevalues of a transmit power, a rank, a precoding matrix, a modulationorder and a transmission scheme using the applied ML decision metric,and cancel the interfering signals from the received signals using thedetermined values of transmit power, rank, precoding matrix, modulationorder and transmission scheme.

Another aspect of the present disclosure provides a method ofconstructing an integrated circuit which includes, but is not limited togenerating a mask layout for a set of features for a layer of theintegrated circuit, wherein the mask layout includes standard celllibrary macros for one or more circuit features that include a processorconfigured to receive a desired signal from a serving base station,receive a plurality of interfering signals from one or more basestations, estimate a maximum likelihood (ML) decision metric of theplurality of interfering signals, apply a logarithm function to the MLdecision metric, and apply a maximum-log approximation function to aserving data vector and an interference data vector, which are includedin the ML decision metric, determine the values of a transmit power, arank, a precoding matrix, a modulation order and a transmission schemeusing the applied ML decision metric, and cancel the interfering signalsfrom the received signals using the determined values of transmit power,rank, precoding matrix, modulation order and transmission scheme.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of the presentdisclosure will become more apparent from the following detaileddescription, when taken in conjunction with the accompanying drawings,in which:

FIG. 1 is a block diagram of an electronic device in a communicationnetwork, according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of a method of estimating interference parametersin a communication network with multiple interfering layers, accordingto an embodiment of the present disclosure;

FIG. 3 is a flowchart of another method of estimating interferenceparameters in a communication network with multiple interfering layers,according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of another method of estimating interferenceparameters in a communication network with multiple interfering layers,according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of a method of testing a processor configured toestimate interference parameters, according to an embodiment of thepresent disclosure; and

FIG. 6 is a flowchart of a method of manufacturing a processorconfigured to estimate interference parameters, according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which embodiments of thepresent disclosure are shown. This disclosure may, however, be embodiedin many different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the device and method to those skilled in the art.In the drawings, the size and relative sizes of layers and regions maybe exaggerated for clarity. Like reference numbers refer to likeelements throughout.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it may be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. As used herein, the term “and/or”includes, but is not limited to, any and all combinations of one or moreof the associated listed items.

It will be understood that, although the terms first, second, and otherterms may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first signal may bereferred to as a second signal, and, similarly, a second signal may bereferred to as a first signal without departing from the teachings ofthe disclosure.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentdevice and method. As used herein, the singular forms “a”, “an” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will be further understood thatthe terms “comprises” and/or “comprising,” or “includes, but is notlimited to” and/or “including, but not limited to” when used in thisspecification, specify the presence of stated features, regions,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

Unless otherwise defined, all terms (including, but not limited totechnical and scientific terms) used herein have the same meanings ascommonly understood by one of ordinary skill in the art to which thepresent device and method belongs. It will be further understood thatterms, such as those defined in commonly used dictionaries, should beinterpreted as having meanings that are consistent with their meaning inthe context of the relevant art and/or the present description, and willnot be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

FIG. 1 is a block diagram of an electronic device in a networkenvironment, according to an embodiment of the present disclosure.

Referring to FIG. 1, an electronic device 100 includes, but is notlimited to, a communication block 110, a processor 120, a memory 130, adisplay 150, an input/output block 160, an audio block 170, a satellitetransceiver, a serving transceiver 180, and an interfering transceiver181. The serving transceiver 180 and the interfering transceiver 181 maybe included in a cellular base station.

The electronic device 100 includes a communication block 110 forconnecting the device 100 to another electronic device or a network forcommunication of voice and data. The communication block 110 providescellular, wide area, local area, personal area, near field, device todevice (D2D), machine to machine (M2M), satellite and short rangecommunications. The functions of the communication block 110, or aportion thereof including a transceiver 113, may be implemented by achipset. In particular, the cellular communications block 112 provides awide area network connection through terrestrial base transceiverstations or directly to other electronic devices, using technologiessuch as D2D, M2M, long term evolution (LTE), fifth generation (5G), longterm evolution advanced (LTE-A), code division multiple access (CDMA),wideband code division multiple access (WCDMA), universal mobiletelecommunications system (UMTS), wireless broadband (WiBro), and globalsystem for mobile communication (GSM). The cellular communications block112 includes, but is not limited to, a chipset and the transceiver 113.The wireless fidelity (WiFi) communications block 114 provides a localarea network connection through network access points using technologiessuch as IEEE 802.11. The Bluetooth communications block 116 providespersonal area direct and networked communications using technologiessuch as IEEE 802.15. The near field communications (NFC) block 118provides point to point short range communications using standards suchas ISO/IEC 14443. The communication block 110 also includes a GNSSreceiver 119. The GNSS receiver 119 may support receiving signals fromthe satellite transmitter.

The interfering transmitter may be associated with at least one of, forexample, a global positioning system (GPS), a global navigationsatellite system (Glonass), a Beidou navigation satellite system(Beidou), and a European global satellite-based navigation system(Galileo). The electronic device 100 may receive electrical power foroperating the functional blocks from a power supply, including, but notlimited to a battery. The serving transceiver 180 may be a part of aterrestrial base transceiver station (BTS) (such as a cellular basestation) and include a radio frequency transmitter and receiverconforming to cellular standards. The serving transceiver 180 mayprovide data and voice communications services to users of mobile userequipment (UE). The interfering transceiver 180 may provide data andvoice communications services to users of mobile user equipment (UE),such as electronic device 100, which are being provided communicationservices in a cell other than the serving cell, such as a neighboringcell. The embodiments of the present disclosure may be extended to acase with multiple interfering transceivers such as an environment inwhich a UE is in range of multiple base stations such as a cellularcommunication system.

The processor 120 provides application layer processing functionsrequired by the user of the electronic device 100. The processor 120also provides command and control functionality for the various blocksin the electronic device 100. The processor 120 provides for updatingcontrol functions required by the functional blocks. The processor 120may provide for coordination of resources required by the transceiver113 including, but not limited to, communication control between thefunctional blocks. The processor 120 may also update the firmware,databases, lookup tables, calibration method programs and librariesassociated with the cellular communications block 112. The cellularcommunications block 112 may also have a local processor or a chipsetwhich dedicates computing resources to cellular communications block 112and other functional blocks required for cellular communication. Theprocessor 120 may execute code which determines values of interferenceparameters associated with interfering signals and cancels theinterfering signals from received signals using the determined valuesinterference parameters including transmit power, rank, precodingmatrix, modulation order and transmission scheme. The rank value mayinclude an integer value between 0 and 3. The precoding matrix mayinclude an integer value between 0 and 7. The transmission scheme valuemay include an integer value between 1 and 7.

The memory 130 provides storage for device control program code, userdata storage, application code and data storage. The memory 130 mayprovide data storage for the firmware, libraries, databases, lookuptables, algorithms, methods, interference parameters, and calibrationdata required by the cellular communications block 112. The program codeand databases required by the cellular communications block 112 may beloaded into local storage within the cellular communications block 112from the memory 130 upon device boot up. The cellular communicationsblock 112 may also have local, volatile and non-volatile memory forstoring the program code, libraries, databases, calibration data andlookup table data.

The display 150 may be a touch panel, and may be embodied as a liquidcrystal display (LCD), organic light emitting diode (OLED) display,active matrix OLED (AMOLED) display, and the like. The input/outputblock 160 controls the interface to the user of the electronic device100. The audio block 170 provides for audio input and output to/from theelectronic device 100.

The serving transceiver 180 may include a base station that is used toreceive, transmit or relay wireless signals. The serving transceiver 180may facilitate communication with the electronic device 100 by sending,receiving, and relaying communication signals to and from the electronicdevice 100. The electronic device 100 may be connected to a networkthrough the serving transceiver 180.

For example, the serving transceiver 180 may be a cell tower, a wirelessrouter, an antenna, multiple antennas, or a combination thereof beingused to send signals to, or receive signals from, the electronic device100, such as a smartphone. The serving transceiver 180 may relay thewireless signals through the network to enable communication with otherelectronic devices 100 such as user equipment (UE), servers or acombination thereof. The serving transceiver 180 may be used to transmitthe communication signals, such as voice or data. The electronic device100 may receive and process signals from the serving transceiver 180.

Based on the communication method, such as code division multiple access(CDMA), orthogonal frequency division multiple access (OFDMA), thirdgeneration partnership project (3GPP) long term evolution (LTE), longterm evolution advanced (LTE-A), fourth generation cellular wirelessstandards (4G), or fifth generation cellular wireless standards (5G),the communication signals may also have reference signals within thecommunicated information. The reference signals may be a predeterminedtraining sequence. The predetermined training sequence may be embeddedwithin the communicated information at a regular time interval.

The serving transceiver 180 may communicate with the electronic device100 through a channel. The channel may encompass frequency, time slot,coding and may include the behavior of the wireless medium, such asreflection, interference and path loss. The serving transceiver 180transmits signals and the electronic device 100 receives the transmittedsignals. However, it is understood that both the electronic device 100and the serving transceiver 180 may each transmit and receive signals.

The communication network may employ a multiple-input andmultiple-output (MIMO) scheme for communicating with the electronicdevice through multiple antennas. A layer may be defined as a set ofinformation communicated through a particular antenna or a particularset of antennas. Each layer may transmit a group of information to aspecific electronic device. The communication network supporting theMIMO scheme may have a transmitted signal that includes various layersincluding a main layer and an interference layer, which may include allsets of information, communicated through the same set of antennas, thatare intended for electronic devices other than the specific electronicdevice.

The main layer is defined as the layer for transmitting information tothe electronic device 100. The interference layer is defined as all ofthe layers for transmitting information to other electronic deviceswhich may be from multiple transceivers, multiple antennas and multiplecells. From the perspective of the electronic device 100, theinformation transmitted to other users through the interference layermay interfere with the information transmitted to the electronic device100 through the main layer.

The main layer may be transmitted according to a main modulation. Themain modulation is defined as the system of signal variations generatedat the serving transceiver 180 in the carrier signal for transmittingthe information to the electronic device 100. The main modulation mayinclude analog or digital modulation methods, such as amplitudemodulation or various keying techniques. For example, the mainmodulation may include phase-shift keying (PSK) such as quadrature PSK(QPSK), frequency-shift keying (FSK), amplitude-shift keying (ASK), 4quadrature amplitude modulation (QAM), 16 QAM, 64 QAM, 256 QAM, 512 QAMand 1024 QAM.

The serving signal transmitted by the serving transceiver 180 mayfurther include a serving reference signal which is known or designatedinformation transmitted by the serving transceiver 180 to determinevarious types of information at a receiving electronic device 100. Theserving reference signal may include a bit, a symbol, a signal pattern,a signal strength, index, code, frequency, phase, duration, or acombination thereof predetermined by the communication network standard(such as 3GPP). The details of the serving reference signal may be knownand used by some or all electronic devices in the communication network.The detail, the structure, the content, or a combination thereof for theserving reference signal may be used by the receiving device, such asthe electronic device 100, to determine information regarding mechanismsused to transmit or receive data.

The communication network may further include an interference signalfrom an interference source generating signals unintended for a specificreceiver. For example, the interfering transceiver 181 source mayinclude various transmitters, including a base station, a relay, arepeater, another electronic device, such as a smart phone or a laptopcomputer, a broadcasting station, or a combination thereof.

According to an embodiment of the present disclosure, an apparatus andmethod are provided for efficient interference parameter estimation. Themethods includes efficient algorithms which approximatemaximum-likelihood (ML) interference parameter estimation while reducingcomputational complexity.

ML parameter estimation may require a summation of exponential functionsover constellations of the serving signal as well as constellations ofinterfering signals. In an embodiment of the present disclosure, amethod is provided which approximates maximum-likelihood (ML)interference parameter estimation by removing a summation overconstellations of a serving signal and providing a multi-dimensionalsummation over constellations of interfering signals with multiplesingle-dimensional summations. Accordingly, the present disclosureprovides a method with decreased computation complexity.

In order to avoid multiplying exponential sums through all searchspaces, the present disclosure provides a method by which the MLdecision metric is applied with a logarithm, and then the maximum-logapproximation is applied to the serving data vector.

In another embodiment of the present disclosure, a method is providedwhich further approximates interference parameter estimation byutilizing approximation techniques disclosed in U.S. Pat. No. 8,953,667,the entire content of which is incorporated herein by reference, foreach single-dimensional summation including Gaussian approximation ofother layer signals and characterization of a bias term as a function ofper-layer modified interference to signal plus noise ratio (ISNR). Themodified ISNR is characterized in that only a residual component of theundesired interference signal contributes to the ISNR value.

According to an embodiment of the present disclosure, a signal receivedby cellular communications block 112 of electronic device 100 may bemodeled by y_(k) at sample k and is defined by Equation (1) below:y _(k)=√{square root over (ρ_(k) ^(S))}H _(k) ^(S) P _(k) ^(S) x _(k)^(S)+√{square root over (ρ)}_(k) ¹ H _(k) ¹ P _(k) ¹ x _(k) ¹ +n_(k).  (1)where y_(k) is an (Nrx×1) vector and Nrx is the number of receiveantennas supporting MIMO in the electronic device 100. The superscript“s” denotes parameters associated with a serving transceiver 180 in aserving cell of the electronic device 100 and the superscript “I”denotes parameters associated with the interfering transceiver 181 inadjacent interference producing cells.

According to an embodiment of the present disclosure, a serving transmitpower level ρ_(k) ^(s), a (Ntx,S×N_(k) ^(s)) serving precoding matrixP_(k) ^(s), a number of serving layers N_(k) ^(s), and modulationorder(s) of (N_(k) ^(s)×1) serving a transmitted signal x_(k) ^(s), areknown at the cellular communications block 112. n_(k) is defined as(Nrx×1) circularly symmetric Gaussian noise. In addition, a (Nrx×Ntx,S)serving channel matrix H_(k) ^(s), and a (Nrx×Ntx,I) interferencechannel matrix 111, are also known. The cellular communications block112 of the electronic device 100 is unaware of an interference transmitpower ρ_(k) ¹, a (Ntx,I×N_(k) ^(I)) interference precoding matrix P_(k)^(I), a number of interference layers N_(k) ^(I), modulation order(s)q_(k) ^(I) of (N_(k) ^(I)×1) interfering transmitted signal 4.

According to an embodiment of the present disclosure, the interferenceparameters ρ_(k) ^(I), P_(k) ^(I), N_(k) ^(I), q_(k) ^(I) are estimatedusing algorithms executed by the processor 120 using code stored in thememory 130. The interference parameters ρ_(k) ^(I), P_(k) ^(I), N_(k)^(I), q_(k) ^(I) have values from finite sets. Further, there are Knumber of samples of an interfering signal with which the parametersρ_(k) ^(I), P_(k) ^(I), NI, q_(k) ^(I) do not vary. The presentdisclosure is based on K number samples, therefore the dependency on kis dropped. In addition, the parameters ρ_(k) ^(S), P_(k) ^(S), N_(k)^(S), q_(k) ^(S) of a serving signal do not vary within K number ofsamples. The metric

_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q) _(I) is computed for everypossible combination of ρ_(k) ^(I), P_(k) ^(I), N_(k) ^(I), q_(k) ^(I)and the selected values of {circumflex over (p)}^(I), {circumflex over(P)}^(I), {circumflex over (N)}^(I), {circumflex over (q)}^(I) are thosewhich maximize

_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q) _(I) . To enable the method of thepresent disclosure, ρ_(k) ^(I), P_(k) ^(I), N_(k) ^(I), q_(k) ^(I) needto belong to a finite set. For example, in Release-12 of the 3GPPstandards for NAICS, ρ_(k) ^(I) belongs to a set of size 3, and N_(k)^(I) belongs to a set of size 2. q_(k) ^(I) belongs to a set of size 3.Set size for P_(k) ^(I) depends on N_(k) ^(I). If N_(k) ^(I) is 1, thenP_(k) ^(I) belongs to a set of size 4. If N_(k) ^(I) is 2, then P_(k)^(I) belongs to a set of size 3.

In the case of a non-space frequency block coding (non-SFBC) servingcell, when the serving signal x_(k) ^(S) has independence with respectto ‘k’, x_(k1) ^(S) and x_(k2) ^(S) are independent if k1≠k2. Oneexample in which a transmitted signal has dependency with respect to ‘k’is space frequency block coding (SFBC). Therefore, in the case of anon-SFBC serving cell, the optimal ML parameter estimation metric isdefined by Equation (2) below:

$\begin{matrix}{{\mathcal{M}_{\;{\rho^{I},P^{I},N^{I},q^{I}}}^{\;{ML}} = {\sum\limits_{k = 1}^{K}\;{\log( {\sum\limits_{x^{S} \in {\prod\limits_{i = 1}^{N^{S}}\chi_{q_{i}^{S}}}}\;{\frac{1}{\prod\limits_{i = 1}^{N^{I}}\;{\chi_{q_{i}^{I}}}}{\sum\limits_{x^{I} \in {\prod\limits_{i = 1}^{N^{I}}\chi_{q_{i}^{I}}}}\;{\exp( {- \frac{{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}x^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}x^{I}}}}^{2}}{\sigma_{n}^{2}}} )}}}} )}}},} & (2)\end{matrix}$where χ_(q) is a set of constellation points for modulation order ‘q’,and σ_(n) ² is a variance of n_(k).

An ML decision generated by using the ML metric defined in Equation (2)above is given as {circumflex over (p)}^(I), {circumflex over (P)}^(I),{circumflex over (N)}^(I), {circumflex over (q)}^(I)=argmax_(ρ) _(I)_(,P) _(I) _(,N) _(I) _(,q) _(I)

_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q) _(I) ^(ML). The ML parameterestimation defined in Equation (2) above assumes that the parametersρ^(I), P^(I), N^(I), q^(I) are estimated based on the above ML decision.However, within the methods provided in the present disclosure, anysubset of such parameters may be estimated when other parameters areknown. The ML method is computationally complex, as compared to othermethods, due to the summation of exponential functions.

In order to reduce computational complexity, a dimension reduced log-map(DR-LM) approximation is defined by Equation (3) below:

$\begin{matrix}{{\mathcal{M}_{\rho^{I},P^{I},N^{I},q^{I}}^{{DL} - {LM}} = {\sum\limits_{k = 1}^{K}\;{\log( \;{\frac{1}{\prod\limits_{i = 1}^{N^{I}}\;{\chi_{q_{i}^{I}}}}{\sum\limits_{x^{I} \in {\prod\limits_{i = 1}^{N^{I}}\chi_{q_{i}^{I}}}}\;{\exp( {- \frac{{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}x^{I}}}}^{2}}{\sigma_{n}^{2}}} )}}} )}}},\mspace{20mu}{{{where}\mspace{14mu}( {{\hat{x}}_{k}^{S},{\hat{x}}_{k}^{I}} )} = {\arg\mspace{11mu}{\min\limits_{x^{S},x^{I}}( {{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}x^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}x^{I}}}} )}}}} & (3)\end{matrix}$creates the minimum Euclidian distance between a serving signal and ahypothetical interfering signal. DR-LM reduces computational complexityby removing the summation over a serving constellation. The DR-LMdecision rule is similar to the ML decision rule by replacing the MLmetric

_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q) _(I) ^(ML) with

_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q) _(I) ^(DR-LM) as {circumflex over(p)}^(I), {circumflex over (P)}^(I), {circumflex over (N)}^(I),{circumflex over (q)}^(I)=argmax_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q)_(I)

_(ρ) _(I) _(,P) _(I) _(,N) _(I) _(,q) _(I) ^(DR-LM). The above decisionrule applies to the methods described in the present disclosure.However, the DR-LM approximation requires the summation of a largenumber of exponential functions when N^(I) is large, thereby increasingits computational complexity.

According to an embodiment of the present disclosure, the DR-LM methoddescribed above may be further reduced in computational complexity by afurther dimension reduced log-map method, hereinafter referred to asFDR-LM1.

The further dimension reduced log-map (FDR-LM1) approximation method isdefined in Equation (4) below:

$\begin{matrix}{{\mathcal{M}_{\rho^{I},P^{I},N^{I},q^{I}}^{{FDR} - {{LM}\; 1}} = {\sum\limits_{k = 1}^{K}\;{\log\mspace{11mu}( \;{\frac{1}{\prod\limits_{i = 1}^{N^{I}}\;{\chi_{q_{i}^{I}}}}\;\begin{Bmatrix}{\sum\limits_{i = 1}^{N^{I}}{\sum\limits_{x^{I} \in \chi_{q_{i}^{I}}}\;{\exp( {- \frac{\begin{matrix}{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} -}} \\{{\sqrt{\rho^{I}}H_{k}^{I}P^{I}{f_{i}( {x^{I},{\hat{x}}_{k}^{I}} )}}}^{2}\end{matrix}}{\sigma_{n}^{2}}} )}}} \\{{- ( {N^{I} - 1} )}\mspace{11mu}{\exp( {- \frac{{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}{\hat{x}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} )}}\end{Bmatrix}} )}}},} & (4)\end{matrix}$where f_(i)(x, w) for a vector w, is defined in Equation (5) below:

$\begin{matrix}{{{jth}\mspace{14mu}{element}\mspace{14mu}{of}\mspace{14mu}{f_{i}( {x,w} )}} = \{ {\begin{matrix}{{{jth}\mspace{14mu}{element}\mspace{14mu}{of}\mspace{14mu} w},} & {{{for}\mspace{14mu} j} \neq i} \\{x,} & {{{for}\mspace{14mu} j} = i}\end{matrix}.} } & (5)\end{matrix}$

FDR-LM is less computationally complex than DR-LM as DR-LM requires aproduct of

$\prod\limits_{i = 1}^{N^{I}}{\chi_{q_{i}^{I}}}$number of exponential functions while FDR-LM has only a summation of

$\sum\limits_{i = 1}^{N^{I}}{\chi_{q_{i}^{I}}}$number of exponential functions. The additional subtraction term

$( {N^{I} - 1} ){\exp( {- \frac{{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}{\hat{x}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} )}$in Equation (4) above is required in order to not over-count minimumdistance contribution. Depending on implementation of the method, theadditional subtraction term may not be necessarily realized as asubtraction, i.e., such subtraction terms may be skipped duringsummation. In this case, the total number of summands becomes

${\sum\limits_{i = 1}^{N^{I}}{\chi_{q_{i}^{I}}}} - {( {N^{I} - 1} ).}$Depending on implementation of the method, the use of {circumflex over(x)}_(k,i) ^(S)(x^(I)) and {circumflex over (x)}_(k,i) ^(I)(x^(I))instead of {circumflex over (x)}_(k) ^(S) and {circumflex over (x)}_(k)^(I) may be desirable where

$( {{{\hat{x}}_{k,i}^{S}( x^{I} )},{{\hat{x}}_{k,i}^{I}( x^{I} )}} ) = {\arg\;{\min\limits_{x^{S},x_{\sim i}^{I}}{( {{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}x^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}x^{I}}}} ).}}}$a_(˜i) of a vector a are all elements of the vector a except for thei-th element.

According to an embodiment of the present disclosure, the FDR-LM methoddescribed above may be further reduced in computational complexity by amethod, hereinafter referred to as FDR-LM2, that does not requireadditional subtraction operations as defined in Equation (6) below:

$\begin{matrix}{\mathcal{M}_{\rho^{I},P^{I},N^{I},q^{I}}^{{FDR} - {{LM}\; 2}} = {\sum\limits_{k = 1}^{K}\;{\log( {\frac{1}{\prod\limits_{i = 1}^{N^{I}}\;{\chi_{q_{i}^{I}}}}{\sum\limits_{i = 1}^{N^{I}}\;{\sum\limits_{x^{I} \in \chi_{q_{i}^{I}}}\;{\exp( {- \frac{{{y_{k} - {\sqrt{\rho^{s}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}{f_{i}( {x^{I},{\hat{x}}_{k}^{I}} )}}}}^{2}}{\sigma_{n}^{2}}} )}}}} )}}} & (6)\end{matrix}$

Similar to FDR-LM1, {circumflex over (x)}_(k,i) ^(S)(x^(I)) and{circumflex over (x)}_(k,i) ^(I)(x^(I)) may be used instead of{circumflex over (x)}_(k) ^(S) and {circumflex over (x)}_(k) ^(I).

According to an embodiment of the present disclosure, the present systemand method provides further approximation of FDR-LM that may removesummation of exponential functions. The present system and methodprovides approximation of FDR-LM2 by providing Gaussian approximation ofan unwanted signal.

The expression y_(k)−√{square root over (ρ^(S))}H_(k)^(S)P^(S){circumflex over (x)}_(k) ^(S)−√{square root over (ρ^(I))}H_(k)^(I)P^(I)f_(i)(x^(I), {circumflex over (x)}_(k) ^(I)) may be re-writtenas y′_(k,i)−√{square root over (ρ^(I))}H_(k) ^(I)P_([:,I]) ^(I)x wherey′_(k,i)=√{square root over (ρ^(I))}H_(k) ^(I)P_([:,I]) ^(I)x_(i)^(I)+ñ_(k,i), and ñ_(k,i)=√{square root over (ρ^(S))}H_(k)^(S)P^(S)(x_(k) ^(S)−{circumflex over (x)}_(k) ^(S))+√{square root over(ρ^(I))}H_(k) ^(I)P_([:,˜i])(x_(k,˜i) ^(I)−{circumflex over (x)}_(k,˜i)^(I))+n_(k). In other words, the summation of |χ_(q) _(i) _(I) |exponential terms for the i-th interference layer in the FDR-LM2 methodmay be considered as a single-layer ML metric with an observationy′_(k,i) and Gaussian noise ñ_(k,i). Since an ML metric with singlelayer transmission is already approximated as described in“Communication System with Modulation Classifier and Method of OperationThereof” (U.S. Pat. No. 8,953,667, the entire content of which isincorporated herein by reference), this approximation technique may beused to approximate FDR-LM. Gaussian approximation of ñ_(k,i) is assumedalthough the distribution is actually not Gaussian.

According to an embodiment of the present disclosure, the FDR-LM2 methoddescribed above may be further reduced in computational complexity byapproximation methods, hereinafter referred to as AFDR-LM2.

The approximated FDR-LM2 (AFDR-LM2) is defined in Equation (7) below:

$\begin{matrix}{{\mathcal{M}_{\rho^{I},P^{I},N^{I},q^{I}}^{{AFDR} - {{LM}\; 2}} = {\sum\limits_{k = 1}^{K}\;\{ {{- \frac{{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}{\hat{x}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} + {\log( {\sum\limits_{i = 1}^{N^{I}}{\exp\{ {\Delta( {q_{i}^{I},\alpha_{i}} )} \}}} )} - {\sum\limits_{i = 1}^{N^{I}}{\log( {\chi_{q_{i}^{I}}} )}}} \}}},} & (7)\end{matrix}$where Δ(⋅,⋅) is a bias function as described in reference U.S. Pat. No.8,953,667.

For each sample k, AFDR-LM2 is realized by determining a minimumEuclidian distance between a serving signal and a hypotheticalinterferer ∥y_(k)−√{square root over (ρ^(S))}H_(k) ^(S)P^(S){circumflexover (x)}_(k) ^(S)−√{square root over (ρ^(I))}H_(k) ^(I)P^(I){circumflexover (x)}_(k) ^(I)∥ and combining with a bias term log (Σ_(i=1) ^(N)^(I) exp{Δ(q_(i) ^(I),α_(i))}). The bias term log(Σ_(i=1) ^(N) ^(I)exp{Δ(q_(i) ^(I),α_(i))}) may use an efficient implementation oflog(Σ_(i=1) ^(N)exp{x_(i)}) which is based on g₂(x₀, x_(i))=max(x₀,x_(i))+log(1+e^(−|x) ⁰ ^(−x) ¹ ^(|))=log(e^(x) ⁰ +e^(x) ¹ ). The biasterm may be computed on a per-layer basis and may be referred to as aper-layer bias.

For a complete characterization of the AFDR-LM2 method, α_(i) isdetermined based on modified per-layer ISNR. cov(ñ_(k,i)) may be acovariance matrix of ñ_(k,i). cov(ñ_(k,i)) may be determined based on asoft decision of {circumflex over (x)}_(k) ^(S) and {circumflex over(x)}_(k,˜i) ^(I). Therefore, α_(i) may be determined asα_(i)=ρ^(I)(P_([:,i]) ^(I))^(H)(H_(k) ^(I))^(H)cov⁻¹(ñ_(k,i))H_(k)^(I)P_([:,i]) ^(I).

According to an embodiment of the present disclosure,

$\alpha_{i} = {\beta\frac{{\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}{\sigma_{n}^{2}}}$may be used, where 0<β<=1 is a discount factor, and

$\frac{{\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}{\sigma_{n}^{2}}$is an interference-to-noise ratio (INR).

The desired analytical value of α_(i) is α_(i)=ρ^(I) (P_([:,i])^(I))^(H)(H_(k) ^(I))^(H)cov⁻¹(ñ_(k,i))H_(k) ^(I)P_([:,i]) ^(I) as shownabove. Since this value may be computationally complex and difficult tocompute, the term

$\frac{{\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}{\sigma_{n}^{2}}$may be used instead to compute an approximation of α_(i). The term

$\frac{{\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}{\sigma_{n}^{2}}$is always greater than or equal to ρ^(I) (P_([:,i]) ^(I))^(H)(H_(k)^(I))^(H)cov⁻¹(ñ_(k,i))H_(k) ^(I)P_([:,i]) ^(I), so a discount factor βneeds to be applied to

$\frac{{\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}{\sigma_{n}^{2}}$

According to another embodiment of the present disclosure,

$\alpha_{i} = {{\gamma\frac{{\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}{\sigma_{n}^{2}}} + {( {1 - \gamma} ){\rho^{I}( P_{\lbrack{:{,i}}\rbrack}^{I} )}^{H}( H_{k}^{I} )^{H}{{cov}^{- 1}( {\overset{\approx}{n}}_{k,i} )}H_{k}^{I}P_{\lbrack{:{,i}}\rbrack}^{I}}}$may be used, where 0<γ<=1, and {tilde over (ñ)}_(k,i)=√{square root over(ρ^(S))}H_(k) ^(S)P^(S)x_(k) ^(S)+√{square root over (ρ^(I))}H_(k)^(I)P_([:,˜i]) ^(I)x_(k,˜i) ^(I)+n_(k). It is noted that ρ^(I)(P_([:,i])^(I))^(H)(H_(k) ^(I))^(H)cov⁻¹({tilde over (ñ)}_(k,i))H_(k)^(I)P_([:,i]) ^(I) is the ISNR of a linear wireless receiver.

According to an embodiment of the present disclosure, the FDR-LM1 methoddescribed above may be further reduced in computational complexity byapproximation methods, hereinafter referred to as AFDR-LM1. The presentapparatus and method provides approximation of FDR-LM1 (AFDR-LM1) byproviding Gaussian approximation of noise.

The approximated FDR-LM1 (AFDR-LM1) is defined in Equation (8) below:

$\begin{matrix}{\mathcal{M}_{\rho^{I},P^{I},N^{I},q^{I}}^{{AFDR} - {{LM}\; 1}} = {\sum\limits_{k = 1}^{K}\;{\{ {{- \frac{{{y_{k} - {\sqrt{\rho^{s}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}{\hat{x}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} + {\log( {{\sum\limits_{i = 1}^{N^{I}}{\exp\{ {\Delta( {q_{i}^{I},\alpha_{i}} )} \}}} - ( {N^{I} - 1} )} )} - {\sum\limits_{i = 1}^{N^{I}}{\log( {\chi_{q_{i}^{I}}} )}}} \}.}}} & (8)\end{matrix}$

According to an embodiment of the present disclosure, log (Σ_(i=1) ^(N)^(I) exp{Δ(q_(i) ^(I),α_(i))}−(N^(I)−1)) may be realized. In oneembodiment, a discount factor as δ log (Σ_(i=1) ^(N) ^(I) exp{Δ(q_(i)^(I),α_(i))}) may be applied, where 0<δ<=1. The present method mayfurther determine α_(i) similarly as described for AFDR-LM2.

In the case of an SFBC serving cell, when the serving signal x_(k) ^(S)has dependency with respect to ‘k’, SFBC may be used for transmission ofthe serving signal. In the case when the serving signal uses SFBC, thereceived signal at two consecutive resource elements (REs) arecorrelated. Two consecutive REs together may be defined by Equation (9)below with SFBC interference and defined in Equation (10) below withnon-SFBC interference.

-   -   With SFBC interference

$\begin{matrix}{\begin{bmatrix}y_{k} \\y_{k + 1}^{*}\end{bmatrix} = {{{{\sqrt{\rho^{S}}\begin{bmatrix}{H_{k}^{S}( {\text{:},1} )} & {- {H_{k}^{S}( {\text{:},2} )}} \\( {H_{k + 1}^{S}( {\text{:},2} )} )^{*} & ( {H_{k + 1}^{S}( {\text{:},1} )} )^{*}\end{bmatrix}}\begin{bmatrix}{x_{k}^{S}(1)} \\( {x_{k}^{S}(2)} )^{*}\end{bmatrix}} + {{\sqrt{\rho^{I}}\begin{bmatrix}{H_{k}^{I}( {\text{:},1} )} & {- {H_{k}^{I}( {\text{:},2} )}} \\( {H_{k + 1}^{I}( {\text{:},2} )} )^{*} & ( {H_{k + 1}^{I}( {\text{:},1} )} )^{*}\end{bmatrix}}\begin{bmatrix}{x_{k}^{I}(1)} \\( {x_{k}^{I}(2)} )^{*}\end{bmatrix}} + \begin{bmatrix}n_{k} \\n_{k + 1}^{*}\end{bmatrix}} = {{{{\sqrt{\rho^{S}}\begin{bmatrix}{\overset{\sim}{H}}_{k}^{S} \\{\overset{\sim}{H}}_{k + 1}^{S}\end{bmatrix}}{\overset{\sim}{x}}_{k}^{S}} + {{\sqrt{\rho^{I}}\begin{bmatrix}{\overset{\sim}{H}}_{k}^{I} \\{\overset{\sim}{H}}_{k + 1}^{I}\end{bmatrix}}{\overset{\sim}{x}}_{k}^{I}} + \begin{bmatrix}n_{k} \\n_{k + 1}^{*}\end{bmatrix}} = {\begin{bmatrix}{\overset{\sim}{y}}_{k} \\{\overset{\sim}{y}}_{k + 1}\end{bmatrix}.}}}} & (9)\end{matrix}$

-   -   With non-SFBC interference

$\begin{matrix}{\begin{bmatrix}y_{k} \\y_{k + 1}^{*}\end{bmatrix} = {{{{\sqrt{\rho^{S}}\begin{bmatrix}{H_{k}^{S}( {\text{:},1} )} & {- {H_{k}^{S}( {\text{:},2} )}} \\( {H_{k + 1}^{S}( {\text{:},2} )} )^{*} & ( {H_{k + 1}^{S}( {\text{:},1} )} )^{*}\end{bmatrix}}\begin{bmatrix}{x_{k}^{S}(1)} \\( {x_{k}^{S}(2)} )^{*}\end{bmatrix}} + {{\sqrt{\rho^{I}}\begin{bmatrix}{H_{k}^{I}P^{I}} & 0 \\0 & ( {H_{k + 1}^{I}P^{I}} )^{*}\end{bmatrix}}\begin{bmatrix}x_{k}^{I} \\( x_{k + 1}^{I} )^{*}\end{bmatrix}} + \begin{bmatrix}n_{k} \\n_{k + 1}^{*}\end{bmatrix}} = {{{\sqrt{\rho^{S}}\begin{bmatrix}{\overset{\sim}{H}}_{k}^{S} \\{\overset{\sim}{H}}_{k + 1}^{S}\end{bmatrix}}{\overset{\sim}{x}}_{k}^{S}} + {{\sqrt{\rho^{I}}\begin{bmatrix}{H_{k}^{I}P^{I}} & 0 \\0 & ( {H_{k + 1}^{I}P^{I}} )^{*}\end{bmatrix}}\begin{bmatrix}x_{k}^{I} \\( x_{k + 1}^{I} )^{*}\end{bmatrix}} + {\begin{bmatrix}n_{k} \\n_{k + 1}^{*}\end{bmatrix}.}}}} & (10)\end{matrix}$

Due to the correlation in the serving signal, 2 REs may be processedtogether to compute a decision metric. The AFDR-LM method may be definedby Equation (11) below with SFBC interference and defined in Equation(12) below with non-SFBC interference.

-   -   With SFBC interference

$\begin{matrix}{\mathcal{M}_{{SFBC},\rho^{I},q^{I}}^{{{AFDR} - {LM}}\;} = {\sum\limits_{{k = 1},{odd}}^{K}{( {{- \frac{\sum\limits_{i = k}^{k + 1}\;{{{\overset{\sim}{y}}_{i} - {\sqrt{\rho^{S}}{\overset{\sim}{H}}_{i}^{S}{\hat{\overset{\sim}{x}}}_{k}^{S}} - {\sqrt{\rho^{I}}{\overset{\sim}{H}}_{i}^{I}{\hat{\overset{\sim}{x}}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} + {\log( {\sum\limits_{i = 1}^{2}\;{\exp\{ {\Delta( {q_{i}^{I},\alpha_{i}} )} \}}} )} - {\sum\limits_{i = 1}^{2}{\log( {\chi_{q_{i}^{I}}} )}} - {\sum\limits_{i = 1}^{2}{\log( {\chi_{q_{i}^{S}}} )}}} ).}}} & (11)\end{matrix}$

-   -   With non-SFBC interference

$\begin{matrix}{\mathcal{M}_{{{non} - {SFBC}}\;,\rho^{I},P^{I},N^{I},q^{I}}^{{{AFDR} - {LM}}\;} = {\sum\limits_{{k = 1},{odd}}^{K}{( {{- \frac{\;\begin{matrix}{{{y_{k} - {\sqrt{\rho^{S}}{\overset{\sim}{H}}_{i}^{S}{\hat{\overset{\sim}{x}}}_{k}^{S}} - {\sqrt{\rho^{I}}{\overset{\sim}{H}}_{k}^{I}P^{I}{\hat{x}}_{k}^{I}}}}^{2} +} \\{{y_{k + 1}^{*} - {\sqrt{\rho^{S}}{\overset{\sim}{H}}_{k + 1}^{S}{\hat{\overset{\sim}{x}}}_{k}^{S}} - {\sqrt{\rho^{I}}( {H_{k + 1}^{I}P^{I}{\hat{x}}_{k + 1}^{I}} )^{*}}}}^{2}\end{matrix}}{\sigma_{n}^{2}}} + {\log( {\sum\limits_{j = 0}^{1}{\sum\limits_{i = 1}^{N^{I}}\;{\exp\{ {\Delta( {q_{i}^{I},\alpha_{{k + j},i}} )} \}}}} )} - {2{\sum\limits_{i = 1}^{N^{I}}{\log( {\chi_{q_{i}^{I}}} )}}} - {\sum\limits_{i = 1}^{2}{\log( {\chi_{q_{i}^{S}}} )}}} ).}}} & (12)\end{matrix}$

When implementing the above expression in Equation (12), the MIMOdetection complexity to find {circumflex over ({tilde over (x)})}_(k)^(S), {circumflex over (x)}_(k) ^(I), {circumflex over (x)}_(k+1) ^(I)considerably increases compared with Equation (7) due to jointprocessing of 2 REs. Equation (11) above does not suffer from suchcomplexity increases since the interference signal x_(k) ^(I) also hascorrelation with respect to ‘k’ due to the SFBC assumption.

One alternative method to applying Equation (12) above may be directlyapplying Equation (7), i.e., ignoring the serving SFBC structure. Withinthe approach of directly applying Equation (7) it is not known whetherinterference uses SFBC or not. Such a determination is part of parameterestimation, and the decision may be made by choosing the one with alarger metric. If Equation (7) is directly used for non-SFBCinterference hypothesis and Equation (11) is used for SFBC-hypothesis,then a processing mismatch may be created between the two hypotheses,which may result in performance degradation. To overcome the potentialperformance degradation issue, an embodiment of the present disclosureincludes an approximation of Equation (12) by applying the bias term toEquation (7) as described in Equations (13) and (14) below.

AFDR-LM for SFBC serving cell:

$\begin{matrix}{{\mathcal{M}_{{{non} - {SFBC}}\;,\rho^{I},P^{I},N^{I},q^{I}}^{{{AFDR} - {LM}}\;} = {\sum\limits_{k = 1}^{K}( {{- \frac{\;{{y_{k} - {\sqrt{\rho^{S}}H_{k}^{S}P^{S}{\hat{x}}_{k}^{S}} - {\sqrt{\rho^{I}}H_{k}^{I}P^{I}{\hat{x}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} + {\log( {\sum\limits_{i = 1}^{N^{I}}\;{\exp\{ {\Delta( {q_{i}^{I},\alpha_{i}} )} \}}} )} + {\Delta^{\prime}( {q^{S},\beta} )} - {\sum\limits_{i = 1}^{N^{I}}{\log( {\chi_{q_{i}^{I}}} )}} - {\sum\limits_{i = 1}^{2}{\log( {\chi_{q_{i}^{S}}} )}}} )}},} & (13) \\{\mathcal{M}_{{SFBC}\;,\rho^{I},q^{I}}^{{{AFDR} - {LM}}\;} = {\sum\limits_{{k = 1},{odd}}^{K}{( {{- \frac{\sum\limits_{i = k}^{k + 1}\;{{{\overset{\sim}{y}}_{i} - {\sqrt{\rho^{S}}{\overset{\sim}{H}}_{k}^{S}P^{S}{\hat{\overset{\sim}{x}}}_{k}^{S}} - {\sqrt{\rho^{I}}{\overset{\sim}{H}}_{k}^{I}{\hat{\overset{\sim}{x}}}_{k}^{I}}}}^{2}}{\sigma_{n}^{2}}} + {\log( {\sum\limits_{i = 1}^{2}\;{\exp\{ {\Delta( {q_{i}^{I},\alpha_{i}} )} \}}} )} - {\sum\limits_{i = 1}^{2}{\log\;( {\chi_{q_{i}^{I}}} )}} - {\sum\limits_{i = 1}^{2}{\log( {\chi_{q_{i}^{S}}} )}}} ).}}} & (14)\end{matrix}$An exemplary characterization of β is given below which corresponds tothe serving+interference to noise ratio (SPINR) and is described inEquation (15) below

$\begin{matrix}{\beta = {\sum\limits_{i = 1}^{Nrx}\;{\frac{1}{\sigma_{n}^{2}{Nrx}}{\{ {{\sum\limits_{j = 1}^{{Ntx},S}\;\frac{{{H_{k}^{S}( {i,j} )}}^{2}}{{Ntx},S}} + {\sum\limits_{j = 1}^{{Ntx},I}\frac{{{H_{k}^{I}( {i,j} )}}^{2}}{{Ntx},I}}} \}.}}}} & (15)\end{matrix}$

FIG. 2 is a flowchart of a method of estimating interference parametersin a communication network with multiple interfering layers, accordingto an embodiment of the present disclosure.

Referring to FIG. 2, at 201, for each interference layer, i, theparameters, {circumflex over (x)}_(k) ^(S) {circumflex over (x)}_(k,˜i)^(I) or the parameters {circumflex over (x)}_(k) ^(S)(x_(i) ^(I)){circumflex over (x)}_(k,˜i) ^(I)(x_(i) ^(I)) are computed. At 202, asum of exponential functions is computed for each interference layer i.At 203, a decision metric is determined by combining all interferencelayers. At 204, a summation is computed of the decision metricdetermined at 203 over multiple samples. At 205, the hypotheticalinterferer is determined using a maximum likelihood decision metric.

FIG. 3 is a flowchart of another method of estimating interferenceparameters in a communication network with multiple interfering layers,according to an embodiment of the present disclosure.

Referring to FIG. 3, at 301, the parameters, {circumflex over (x)}_(k)^(S), {circumflex over (x)}_(k) ^(I) are computed to determine a minimumdistance between a serving signal and a hypothetical interfering signal.At 302, a modified ISNR is computed for each interference layer, i, anda bias term is computed. The bias term compensates for a differencebetween the FDR-LM metric and the minimum distance term for eachcandidate group of interference parameters. The value to be applied tothe bias term may be stored in a look up table (LUT) in memory 130 forretrieval by the processor 120. The LUT may record a bias value due to adifference between the FDR-LM metric and the minimum distance term foreach candidate group of interference parameters with respect to a givenINR. At 303, a decision metric is determined by combining the minimumEuclidian distance between a serving signal and a hypotheticalinterfering signal determined at 301 with the bias term computed at 302.At 304, a summation is computed of the decision metrics determined at303 over multiple samples. At 305, the hypothetical interferer isdetermined using a maximum likelihood decision metric.

FIG. 4 is a flowchart of another method of estimating interferenceparameters in a communication network with multiple interfering layers,according to an embodiment of the present disclosure.

Referring to FIG. 4, at 401, a determination is made whether a metriccorresponds to an SFBC interference hypothesis. If yes, then at 402, ajoint signal model is constructed using 2 consecutive SFBC resourceelement samples. At 403, a decision metric is computed using the methodillustrated in the flowchart of FIG. 3. At 407, the hypotheticalinterferer is determined using a maximum likelihood metric.

If the decision at 401 is no, then at 404 a decision metric is computedusing the method illustrated in the flowchart of FIG. 3. At 405 a signalplus interference noise ratio (SPINR) is computed using an average overall layers and a bias term is determined. At 406, a decision metric iscompleted by combining the decision metric determined at 404 with thebias term determined at 405. At 407, the hypothetical interferer isdetermined using a maximum likelihood metric.

FIG. 5 is a flowchart of a method of testing a processor configured todetermine interference parameters according to an embodiment of thepresent disclosure, where the processor is either implemented inhardware or implemented in hardware that is programmed with software.

Referring to FIG. 5, the method, at 501, forms the processor as part ofa wafer or package that includes at least one other processor. Theprocessor is configured to receive a desired signal from a serving basestation, receive a plurality of interfering signals from one or morebase stations, determine a maximum likelihood (ML) decision metric todetermine a value of a transmit power, a value of a rank, a value of aprecoding matrix, a value of a modulation and a value of a transmissionscheme of the plurality of interfering signals, apply a logarithmfunction to the ML decision metric, and apply a maximum-logapproximation function to a serving data vector and an interference datavector, which are included in the ML decision metric, determine thevalues of transmit power, rank, precoding matrix, modulation order andtransmission scheme using the applied ML decision metric, and cancel theinterfering signals from the received signals using the determinedvalues of transmit power, rank, precoding matrix, modulation order andtransmission scheme.

At 503, the method tests the processor. Testing the processor includestesting the processor and the at least one other processor using one ormore electrical to optical converters, one or more optical splittersthat split an optical signal into two or more optical signals, and oneor more optical to electrical converters.

FIG. 6 is a flowchart of constructing an integrated circuit, accordingto an embodiment of the present disclosure.

Referring to FIG. 6, the method, at 601, comprises the initial layout ofdata in which the method generates a mask layout for a set of featuresfor a layer of the integrated circuit. The mask layout includes standardcell library macros for one or more circuit features that include aprocessor. The processor is configured to receive a desired signal froma serving base station, receive a plurality of interfering signals fromone or more base stations, determine a maximum likelihood (ML) decisionmetric to determine a value of a transmit power, a value of a rank, avalue of a precoding matrix, a value of a modulation and a value of atransmission scheme of the plurality of interfering signals, apply alogarithm function to the ML decision metric, and apply a maximum-logapproximation function to a serving data vector and an interference datavector, which are included in the ML decision metric, determine thevalues of transmit power, rank, precoding matrix, modulation order andtransmission scheme using the applied ML decision metric, and cancel theinterfering signals from the received signals using the determinedvalues of transmit power, rank, precoding matrix, modulation order andtransmission scheme.

At 603, there is a design rule check in which the method disregardsrelative positions of the macros for compliance to layout design rulesduring the generation of the mask layout.

At 605, there is an adjustment of the layout in which the method checksthe relative positions of the macros for compliance to layout designrules after generating the mask layout.

At 607, a new layout design is made, in which the method, upon detectionof noncompliance with the layout design rules by any of the macros,modifies the mask layout by modifying each of the noncompliant macros tocomply with the layout design rules, generates a mask according to themodified mask layout with the set of features for the layer of theintegrated circuit and manufactures the integrated circuit layeraccording to the mask.

While the present disclosure has been particularly shown and describedwith reference to certain embodiments thereof, it will be understood bythose of ordinary skill in the art that various changes in form anddetails may be made therein without departing from the spirit and scopeof the present disclosure as defined by the appended claims and theirequivalents.

What is claimed is:
 1. A method, comprising: receiving a desired signalfrom a serving base station; receiving a plurality of interferingsignals from one or more base stations; estimating a maximum likelihood(ML) decision metric of the plurality of interfering signals; applying alogarithm function to the ML decision metric, and applying a maximum-logapproximation function to a serving data vector and an interference datavector, which are included in the ML decision metric; determining thevalues of a transmit power, a rank, a precoding matrix, a modulationorder, and a transmission scheme using the applied ML decision metric;and cancelling the interfering signals from the received signal usingthe determined values of the transmit power, the rank, the precodingmatrix, the modulation order, and the transmission scheme.
 2. The methodof claim 1, wherein at least one of the desired signal and theinterfering signal are transmitted from at least one base station withmultiple input multiple output (MIMO) antennas.
 3. The method of claim1, wherein the modulation order includes at least one of 4 quadratureamplitude modulation (QAM), 16 QAM, 64 QAM, 256 QAM, 512 QAM, 1024 QAM,2048 QAM and 4096 QAM.
 4. The method of claim 1, wherein the rank valueincludes a value belonging to a finite set.
 5. The method of claim 1,wherein the precoding matrix value includes a value belonging to afinite set.
 6. The method of claim 1, wherein the transmission schemevalue includes a value belonging to a finite set.
 7. The method of claim1, wherein the applied ML decision metric includes a bias term whichreduces performance degradation in a cellular communication system dueto the approximation of the maximum log function.
 8. The method of claim7, wherein the bias term compensates for a difference between a furtherdimension reduced log-map (FDR-LM) metric and a minimum Euclideandistance value for each candidate group of values of transmit power,rank, precoding matrix, modulation order and transmission scheme.
 9. Themethod of claim 8, wherein the bias term is computed using a per-layerbias wherein the per-layer bias is computed using a per-layer modifiedinterference to signal plus noise ratio (ISNR) approximation of otherlayer signals.
 10. The method of claim 1, wherein estimating the MLdecision metric comprises multiple single-dimensional summations of theinterfering signals.
 11. An apparatus, comprising: a processorconfigured to: receive a desired signal from a serving base station;receive a plurality of interfering signals from one or more basestations; estimate a maximum likelihood (ML) decision metric of theplurality of interfering signals; apply a logarithm function to the MLdecision metric, and apply a maximum-log approximation function to aserving data vector and an interference data vector, which are includedin the ML decision metric; determine the values of a transmit power, arank, a precoding matrix, a modulation order and a transmission schemeusing the applied ML decision metric; and cancel the interfering signalsfrom the received signal using the determined values of the transmitpower, the rank, the precoding matrix, the modulation order, and thetransmission scheme.
 12. The apparatus of claim 11, further comprisingmultiple input multiple output (MIMO) antennas, wherein at least one ofthe desired signal and the interfering signal are transmitted from abase station to the MIMO antennas.
 13. The apparatus of claim 11,wherein the modulation order includes at least one of 4 quadratureamplitude modulation (QAM), 16 QAM, 64 QAM, 256 QAM, 512 QAM and 1024QAM.
 14. The apparatus of claim 11, wherein the rank value includes avalue belonging to a finite set.
 15. The apparatus of claim 11, whereinthe precoding matrix value includes a value belonging to a finite set.16. The apparatus of claim 11, wherein the transmission scheme valueincludes a value belonging to a finite set.
 17. The apparatus of claim11, wherein the applied ML decision metric includes a bias term whichreduces performance degradation in the cellular communication system dueto the approximation of the maximum log function.
 18. The apparatus ofclaim 17, wherein the bias term compensates for a difference between afurther dimension reduced log-map (FDR-LM) metric and a minimumEuclidean distance value for each candidate group of values of transmitpower, rank, precoding matrix, modulation order and transmission scheme.19. The apparatus of claim 18, wherein the processor is furtherconfigured to compute the bias term using a per-layer bias wherein theper-layer bias is computed using a per-layer modified interference tosignal plus noise ratio (ISNR) approximation of other layer signals. 20.The apparatus of claim 11, wherein the processor is further configuredto estimate the ML decision metric using multiple single-dimensionalsummations of the interfering signals.